Take Home Exercise 1

Published

February 9, 2023

Modified

February 11, 2023

1.0 Overview

1.1 Background

This analysis aims to apply appropriate spatial point patterns analysis methods to discover the geographical distribution of functional and non-function water points and their co-locations if any in Osun State, Nigeria.

1.2 Task

  • Exploratory Spatial Data Analysis (ESDA)

  • Second-order Spatial Point Pattern Analysis

  • Spatial Correlation Analysis

2.0 Setup

2.1 Import Packages

  • sf

  • tidyVerse

  • tmap, maptools, kableExtra - Used for visualizing dataframes and plots

pacman::p_load(sf, maptools, raster, spatstat, tmap, kableExtra, tidyverse, funModeling, sfdep)

3.0 Data Wrangling

3.1 Datasets Used

Code 
# initialise a dataframe of our geospatial and aspatial dataset details
datasets <- data.frame(
  Type=c("Geospatial",
         "Geospatial",
         "Geospatial",
         "Geospatial",
         "Geospatial",
         "Geospatial",
         "Geospatial",
         "Geospatial",
         "Geospatial",
         "Geospatial",
         "Geospatial",
         "Geospatial",
         "Geospatial",
         "Geospatial",
         
         "Aspatial"),
  
  Name=c("geoBoundaries-NGA-ADM2",
         "geoBoundaries-NGA-ADM2",
         "geoBoundaries-NGA-ADM2",
         "geoBoundaries-NGA-ADM2",
         "geoBoundaries-NGA-ADM2",
         "geoBoundaries-NGA-ADM2",
         "nga_admbnda_adm2_osgof_20190417",
         "nga_admbnda_adm2_osgof_20190417",
         "nga_admbnda_adm2_osgof_20190417",
         "nga_admbnda_adm2_osgof_20190417",
         "nga_admbnda_adm2_osgof_20190417",
         "nga_admbnda_adm2_osgof_20190417",
         "nga_admbnda_adm2_osgof_20190417",
         "nga_admbnda_adm2_osgof_20190417",
         
         "WPdx"),
  
  Format=c(".dbf", 
           ".geojson", 
           ".prj", 
           ".shp", 
           ".shx", 
           ".topojson",
           ".CPG",
           ".dbf",
           ".prj",
           ".sbn", 
           ".sbx", 
           ".shp", 
           ".shp", 
           ".shx", 
          
           ".csv"),
  
  Source=c("[geoBoundaries](https://www.geoboundaries.org/index.html#getdata)",
           "[geoBoundaries](https://www.geoboundaries.org/index.html#getdata)",
           "[geoBoundaries](https://www.geoboundaries.org/index.html#getdata)",
           "[geoBoundaries](https://www.geoboundaries.org/index.html#getdata)",
           "[geoBoundaries](https://www.geoboundaries.org/index.html#getdata)",
           "[geoBoundaries](https://www.geoboundaries.org/index.html#getdata)",
           
          "[Humanitarian Data Exchange](https://data.humdata.org/dataset/cod-ab-nga)",
           "[Humanitarian Data Exchange](https://data.humdata.org/dataset/cod-ab-nga)",
           "[Humanitarian Data Exchange](https://data.humdata.org/dataset/cod-ab-nga)",
           "[Humanitarian Data Exchange](https://data.humdata.org/dataset/cod-ab-nga)",
           "[Humanitarian Data Exchange](https://data.humdata.org/dataset/cod-ab-nga)",
           "[Humanitarian Data Exchange](https://data.humdata.org/dataset/cod-ab-nga)",
           "[Humanitarian Data Exchange](https://data.humdata.org/dataset/cod-ab-nga)",
           "[Humanitarian Data Exchange](https://data.humdata.org/dataset/cod-ab-nga)",
           
           "[ WPdx Global Data Repositories](https://www.waterpointdata.org/access-data/)")
  )

# with reference to this guide on kableExtra:
# https://cran.r-project.org/web/packages/kableExtra/vignettes/awesome_table_in_html.html
# kable_material is the name of the kable theme
# 'hover' for to highlight row when hovering, 'scale_down' to adjust table to fit page width
library(knitr)
library(kableExtra)
kable(datasets, caption="Datasets Used") %>%
  kable_material("hover", latex_options="scale_down")
Datasets Used
Type Name Format Source
Geospatial geoBoundaries-NGA-ADM2 .dbf [geoBoundaries](https://www.geoboundaries.org/index.html#getdata)
Geospatial geoBoundaries-NGA-ADM2 .geojson [geoBoundaries](https://www.geoboundaries.org/index.html#getdata)
Geospatial geoBoundaries-NGA-ADM2 .prj [geoBoundaries](https://www.geoboundaries.org/index.html#getdata)
Geospatial geoBoundaries-NGA-ADM2 .shp [geoBoundaries](https://www.geoboundaries.org/index.html#getdata)
Geospatial geoBoundaries-NGA-ADM2 .shx [geoBoundaries](https://www.geoboundaries.org/index.html#getdata)
Geospatial geoBoundaries-NGA-ADM2 .topojson [geoBoundaries](https://www.geoboundaries.org/index.html#getdata)
Geospatial nga_admbnda_adm2_osgof_20190417 .CPG [Humanitarian Data Exchange](https://data.humdata.org/dataset/cod-ab-nga)
Geospatial nga_admbnda_adm2_osgof_20190417 .dbf [Humanitarian Data Exchange](https://data.humdata.org/dataset/cod-ab-nga)
Geospatial nga_admbnda_adm2_osgof_20190417 .prj [Humanitarian Data Exchange](https://data.humdata.org/dataset/cod-ab-nga)
Geospatial nga_admbnda_adm2_osgof_20190417 .sbn [Humanitarian Data Exchange](https://data.humdata.org/dataset/cod-ab-nga)
Geospatial nga_admbnda_adm2_osgof_20190417 .sbx [Humanitarian Data Exchange](https://data.humdata.org/dataset/cod-ab-nga)
Geospatial nga_admbnda_adm2_osgof_20190417 .shp [Humanitarian Data Exchange](https://data.humdata.org/dataset/cod-ab-nga)
Geospatial nga_admbnda_adm2_osgof_20190417 .shp [Humanitarian Data Exchange](https://data.humdata.org/dataset/cod-ab-nga)
Geospatial nga_admbnda_adm2_osgof_20190417 .shx [Humanitarian Data Exchange](https://data.humdata.org/dataset/cod-ab-nga)
Aspatial WPdx .csv [ WPdx Global Data Repositories](https://www.waterpointdata.org/access-data/)

3.2 Geospatial Data

3.2.1 Load Data

NGA <- st_read("data/geospatial", 
                  layer = "nga_admbnda_adm2_osgof_20190417") %>%
  filter(ADM1_EN == "Osun") %>%
  st_transform(crs = 26392)
Reading layer `nga_admbnda_adm2_osgof_20190417' from data source 
  `/Users/shambhavigoenka/Desktop/School/Geo/IS415-GAA/take_home_ex/take_home_ex01/data/geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 774 features and 16 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 2.668534 ymin: 4.273007 xmax: 14.67882 ymax: 13.89442
Geodetic CRS:  WGS 84
glimpse(NGA)
Rows: 30
Columns: 17
$ Shape_Leng <dbl> 1.7951405, 0.7101503, 0.9199564, 0.8502782, 0.5212768, 0.60…
$ Shape_Area <dbl> 0.062436080, 0.024818478, 0.038002894, 0.030445804, 0.01221…
$ ADM2_EN    <chr> "Aiyedade", "Aiyedire", "Atakumosa East", "Atakumosa West",…
$ ADM2_PCODE <chr> "NG030001", "NG030002", "NG030003", "NG030004", "NG030005",…
$ ADM2_REF   <chr> "Aiyedade", "Aiyedire", "Atakumosa East", "Atakumosa West",…
$ ADM2ALT1EN <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ ADM2ALT2EN <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ ADM1_EN    <chr> "Osun", "Osun", "Osun", "Osun", "Osun", "Osun", "Osun", "Os…
$ ADM1_PCODE <chr> "NG030", "NG030", "NG030", "NG030", "NG030", "NG030", "NG03…
$ ADM0_EN    <chr> "Nigeria", "Nigeria", "Nigeria", "Nigeria", "Nigeria", "Nig…
$ ADM0_PCODE <chr> "NG", "NG", "NG", "NG", "NG", "NG", "NG", "NG", "NG", "NG",…
$ date       <date> 2016-11-29, 2016-11-29, 2016-11-29, 2016-11-29, 2016-11-29…
$ validOn    <date> 2019-04-17, 2019-04-17, 2019-04-17, 2019-04-17, 2019-04-17…
$ validTo    <date> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ SD_EN      <chr> "Osun West", "Osun West", "Osun East", "Osun East", "Osun C…
$ SD_PCODE   <chr> "NG03003", "NG03003", "NG03002", "NG03002", "NG03001", "NG0…
$ geometry   <MULTIPOLYGON [m]> MULTIPOLYGON (((213526.6 34..., MULTIPOLYGON (…

3.2.2 Data Preprocessing

3.2.2.1 Excluding redundant fields

This is because

NGA <- NGA %>%
  select(c(3:4, 8:9)) #dplyr

3.2.2.2 Invalid Geometries

length(which(st_is_valid(NGA) == FALSE))
[1] 0

Everything is valid

3.2.2.3 Checking for Duplicate Names

NGA$ADM2_EN[duplicated(NGA$ADM2_EN)==TRUE]
character(0)

No duplicate names

3.2.2.4 Remove Missing Values

NGA[rowSums(is.na(NGA))!=0,]
Simple feature collection with 0 features and 4 fields
Bounding box:  xmin: NA ymin: NA xmax: NA ymax: NA
Projected CRS: Minna / Nigeria Mid Belt
[1] ADM2_EN    ADM2_PCODE ADM1_EN    ADM1_PCODE geometry  
<0 rows> (or 0-length row.names)

3.2.3 Initial Visualisation

plot(st_geometry(NGA))

3.3 Aspatial Data

3.3.1 Load Data

wp_nga <- read_csv("data/aspatial/WPdx.csv") %>%
  filter(`#clean_country_name` == "Nigeria" & `#clean_adm1` == "Osun")
glimpse(wp_nga)
Rows: 5,745
Columns: 74
$ row_id                        <dbl> 225950, 225524, 197014, 225173, 225843, …
$ `#source`                     <chr> "Federal Ministry of Water Resources, Ni…
$ `#lat_deg`                    <dbl> 7.433597, 7.778512, 7.490558, 7.925342, …
$ `#lon_deg`                    <dbl> 4.262443, 4.563097, 4.531398, 4.727448, …
$ `#report_date`                <chr> "05/05/2015 12:00:00 AM", "04/22/2015 12…
$ `#status_id`                  <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes"…
$ `#water_source_clean`         <chr> "Borehole", "Protected Hand Dug Well", "…
$ `#water_source_category`      <chr> "Well", "Well", "Well", "Well", "Well", …
$ `#water_tech_clean`           <chr> "Hand Pump", "Hand Pump", "Mechanized Pu…
$ `#water_tech_category`        <chr> "Hand Pump", "Hand Pump", "Mechanized Pu…
$ `#facility_type`              <chr> "Improved", "Improved", "Improved", "Imp…
$ `#clean_country_name`         <chr> "Nigeria", "Nigeria", "Nigeria", "Nigeri…
$ `#clean_adm1`                 <chr> "Osun", "Osun", "Osun", "Osun", "Osun", …
$ `#clean_adm2`                 <chr> "Irewole", "Olorunda", "Ife North", "Bor…
$ `#clean_adm3`                 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ `#clean_adm4`                 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ `#install_year`               <dbl> 2005, 2010, 2007, 2012, NA, 1987, NA, NA…
$ `#installer`                  <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ `#rehab_year`                 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ `#rehabilitator`              <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ `#management_clean`           <chr> "Community Management", "Community Manag…
$ `#status_clean`               <chr> "Functional", "Functional", "Functional,…
$ `#pay`                        <chr> "No", "No", "No", "No", "No", "No", "No"…
$ `#fecal_coliform_presence`    <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ `#fecal_coliform_value`       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ `#subjective_quality`         <chr> "Acceptable quality", "Acceptable qualit…
$ `#activity_id`                <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "ceb…
$ `#scheme_id`                  <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ `#wpdx_id`                    <chr> "6FV6C7M6+CXP", "6FV6QHH7+C65", "6FV6FGR…
$ `#notes`                      <chr> "WAASINMI COM. HIGH SCHOOL", "St. James …
$ `#orig_lnk`                   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "htt…
$ `#photo_lnk`                  <chr> NA, NA, NA, NA, "https://akvoflow-55.s3.…
$ `#country_id`                 <chr> "NG", "NG", "NG", "NG", "NG", "NG", "NG"…
$ `#data_lnk`                   <chr> "https://catalog.waterpointdata.org/data…
$ `#distance_to_primary_road`   <dbl> 8247.06896, 311.07585, 68.23175, 7363.55…
$ `#distance_to_secondary_road` <dbl> 9515.704491, 265.095706, 19.847274, 1212…
$ `#distance_to_tertiary_road`  <dbl> 6975.977356, 174.968698, 1751.491632, 15…
$ `#distance_to_city`           <dbl> 32331.378, 1018.195, 2431.608, 25054.146…
$ `#distance_to_town`           <dbl> 12718.2161, 7732.5919, 37640.5390, 5449.…
$ water_point_history           <chr> "{\"2015-05-05\": {\"source\": \"Federal…
$ rehab_priority                <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 0…
$ water_point_population        <dbl> 53, 0, 1674, 2420, 0, 380, 2754, 61, 253…
$ local_population_1km          <dbl> 187, 194, 4482, 22568, 0, 380, 7338, 100…
$ crucialness_score             <dbl> 0.28342246, NA, 0.37349398, 0.10723148, …
$ pressure_score                <dbl> 0.1766667, NA, 1.6740000, 8.0666667, NA,…
$ usage_capacity                <dbl> 300, 300, 1000, 300, 300, 300, 1000, 250…
$ is_urban                      <lgl> FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, TR…
$ days_since_report             <dbl> 2812, 2825, 2817, 2815, 2809, 2820, 2804…
$ staleness_score               <dbl> 41.21452, 41.04598, 41.14962, 41.17557, …
$ latest_record                 <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE…
$ location_id                   <dbl> 239286, 231055, 235228, 236811, 242436, …
$ cluster_size                  <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ `#clean_country_id`           <chr> "NGA", "NGA", "NGA", "NGA", "NGA", "NGA"…
$ `#country_name`               <chr> "Nigeria", "Nigeria", "Nigeria", "Nigeri…
$ `#water_source`               <chr> "Improved Tube well or borehole", "Impro…
$ `#water_tech`                 <chr> "Hand pump", "Manual", "Motorised", "Han…
$ `#status`                     <chr> "Functional (and in use)", "Functional (…
$ `#adm2`                       <chr> "Irewole", "Olorunda", "Ife Central", "B…
$ `#adm3`                       <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ `#management`                 <chr> "Community Management", "Community Manag…
$ `#adm1`                       <chr> "Osun", "Osun", "Osun", "Osun", "Osun", …
$ `New Georeferenced Column`    <chr> "POINT (4.2624433 7.4335967)", "POINT (4…
$ lat_deg_original              <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ lat_lon_deg                   <chr> "(7.4335967°, 4.2624433°)", "(7.7785117°…
$ lon_deg_original              <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ public_data_source            <chr> "https://catalog.waterpointdata.org/data…
$ converted                     <chr> "#status_id, #water_source, #pay, #statu…
$ count                         <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ created_timestamp             <chr> "06/30/2020 12:56:07 PM", "06/30/2020 12…
$ updated_timestamp             <chr> "06/30/2020 12:56:07 PM", "06/30/2020 12…
$ `#pay_clean`                  <chr> "No payment", "No payment", "No payment"…
$ `#subjective_quality_clean`   <chr> "Acceptable quality", "Acceptable qualit…
$ is_duplicate                  <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ dataset_title                 <chr> "Federal Ministry of Water Resources_Nig…

3.3.2 Data Preprocessing

3.3.2.1 Convert point data to sf point features

First convert wkt field into sfc field. Then convert it into sf object.

wp_nga$Geometry = st_as_sfc(wp_nga$`New Georeferenced Column`)

wp_sf <- st_sf(wp_nga, crs=4326)

3.3.2.2 Transform into Coordinate System

wp_sf <- wp_sf %>%
  st_transform(crs = 26392)

3.3.2.3 Handle Missing Data

freq(data = wp_sf,
     input = '#status_clean')

              #status_clean frequency percentage cumulative_perc
1                Functional      2406      41.88           41.88
2            Non-Functional      2086      36.31           78.19
3                      <NA>       748      13.02           91.21
4  Functional, needs repair       259       4.51           95.72
5       Non-Functional, dry       159       2.77           98.49
6    Functional, not in use        64       1.11           99.60
7  Abandoned/Decommissioned        15       0.26           99.86
8 Functional but not in use         8       0.14          100.00

There are eight classes in the #status_clean fields.

To change NA with “unknown”

wp_sf_nga <- wp_sf %>% 
  rename(status_clean = '#status_clean') %>%
  select(status_clean) %>%
  mutate(status_clean = replace_na(
    status_clean, "unknown"))

3.3.3 Water Point Extraction

Functional Points

wp_functional <- wp_sf_nga %>%
  filter(status_clean %in%
           c("Functional",
             "Functional, needs repair",
             "Functional, not in use",
             "Functional but not in use"))

Non-Functional Points

wp_nonfunctional <- wp_sf_nga %>%
  filter(status_clean %in%
           c("Abandoned/Decommissioned",
             "Non-Functional, dry",
             "Non-Functional"))

Unknown Status Points

wp_unknown <- wp_sf_nga %>%
  filter(status_clean == "unknown")
freq(data = wp_functional,
     input = 'status_clean')

               status_clean frequency percentage cumulative_perc
1                Functional      2406      87.91           87.91
2  Functional, needs repair       259       9.46           97.37
3    Functional, not in use        64       2.34           99.71
4 Functional but not in use         8       0.29          100.00
freq(data = wp_nonfunctional,
     input = 'status_clean')

              status_clean frequency percentage cumulative_perc
1           Non-Functional      2086      92.30           92.30
2      Non-Functional, dry       159       7.04           99.34
3 Abandoned/Decommissioned        15       0.66          100.00
freq(data = wp_unknown,
     input = 'status_clean')

  status_clean frequency percentage cumulative_perc
1      unknown       748        100             100

3.3.4 Point-in-Polygon Count

Extract number of total, functional, nonfunctional and unknown water points

NGA_wp <- NGA %>% 
  mutate(`total_wp` = lengths(
    st_intersects(NGA, wp_sf_nga))) %>%
  mutate(`wp_functional` = lengths(
    st_intersects(NGA, wp_functional))) %>%
  mutate(`wp_nonfunctional` = lengths(
    st_intersects(NGA, wp_nonfunctional))) %>%
  mutate(`wp_unknown` = lengths(
    st_intersects(NGA, wp_unknown)))

3.4 Combined Data Wrangling

3.4.1 Convert sf dataframes to sp Spatial class

wp_functional_spat = as_Spatial(wp_functional)
wp_nonfunctional_spat = as_Spatial(wp_nonfunctional)
NGA_spat = as_Spatial(NGA)

Peek

wp_functional_spat
class       : SpatialPointsDataFrame 
features    : 2737 
extent      : 177285.9, 290751, 343128.1, 450859.7  (xmin, xmax, ymin, ymax)
crs         : +proj=tmerc +lat_0=4 +lon_0=8.5 +k=0.99975 +x_0=670553.98 +y_0=0 +a=6378249.145 +rf=293.465 +towgs84=-92,-93,122,0,0,0,0 +units=m +no_defs 
variables   : 1
names       :           status_clean 
min values  :             Functional 
max values  : Functional, not in use 
wp_nonfunctional_spat
class       : SpatialPointsDataFrame 
features    : 2260 
extent      : 180539, 290616, 340054.1, 450780.1  (xmin, xmax, ymin, ymax)
crs         : +proj=tmerc +lat_0=4 +lon_0=8.5 +k=0.99975 +x_0=670553.98 +y_0=0 +a=6378249.145 +rf=293.465 +towgs84=-92,-93,122,0,0,0,0 +units=m +no_defs 
variables   : 1
names       :             status_clean 
min values  : Abandoned/Decommissioned 
max values  :      Non-Functional, dry 
NGA_spat
class       : SpatialPolygonsDataFrame 
features    : 30 
extent      : 176503.2, 291043.8, 331434.7, 454520.1  (xmin, xmax, ymin, ymax)
crs         : +proj=tmerc +lat_0=4 +lon_0=8.5 +k=0.99975 +x_0=670553.98 +y_0=0 +a=6378249.145 +rf=293.465 +towgs84=-92,-93,122,0,0,0,0 +units=m +no_defs 
variables   : 4
names       :  ADM2_EN, ADM2_PCODE, ADM1_EN, ADM1_PCODE 
min values  : Aiyedade,   NG030001,    Osun,      NG030 
max values  :   Osogbo,   NG030030,    Osun,      NG030

3.4.2 Convert sp Spatial to generic sp format

wp_functional_sp <- as(wp_functional_spat, "SpatialPoints")
wp_nonfunctional_sp <- as(wp_nonfunctional_spat, "SpatialPoints")
NGA_sp <-as(NGA_spat, "SpatialPolygons")

Peek

wp_functional_sp
class       : SpatialPoints 
features    : 2737 
extent      : 177285.9, 290751, 343128.1, 450859.7  (xmin, xmax, ymin, ymax)
crs         : +proj=tmerc +lat_0=4 +lon_0=8.5 +k=0.99975 +x_0=670553.98 +y_0=0 +a=6378249.145 +rf=293.465 +towgs84=-92,-93,122,0,0,0,0 +units=m +no_defs 
wp_nonfunctional_sp
class       : SpatialPoints 
features    : 2260 
extent      : 180539, 290616, 340054.1, 450780.1  (xmin, xmax, ymin, ymax)
crs         : +proj=tmerc +lat_0=4 +lon_0=8.5 +k=0.99975 +x_0=670553.98 +y_0=0 +a=6378249.145 +rf=293.465 +towgs84=-92,-93,122,0,0,0,0 +units=m +no_defs 
NGA_sp
class       : SpatialPolygons 
features    : 30 
extent      : 176503.2, 291043.8, 331434.7, 454520.1  (xmin, xmax, ymin, ymax)
crs         : +proj=tmerc +lat_0=4 +lon_0=8.5 +k=0.99975 +x_0=670553.98 +y_0=0 +a=6378249.145 +rf=293.465 +towgs84=-92,-93,122,0,0,0,0 +units=m +no_defs

3.4.3 Convert generic sp to spatstat ppp format

# from sp object, convert into ppp format
wp_functional_ppp <- as(wp_functional_sp, "ppp")
wp_nonfunctional_ppp <- as(wp_nonfunctional_sp, "ppp")

View

par(mfrow=c(1,2))
plot(wp_functional_ppp)
plot(wp_nonfunctional_ppp)

3.4.3.1 Check for Duplication

any(duplicated(wp_functional_ppp)); any(duplicated(wp_nonfunctional_ppp)) 
[1] FALSE
[1] FALSE

No duplicates means no jittering needed

3.4.4 Create Owin object

NGA_owin <- as(NGA_sp, "owin")
plot(NGA_owin)

3.4.4 Combine point event object with Owin object

wp_functional_ppp = wp_functional_ppp[NGA_owin]
wp_nonfunctional_ppp = wp_nonfunctional_ppp[NGA_owin]

View

par(mfrow=c(1,2))
plot(wp_functional_ppp)
plot(wp_nonfunctional_ppp)

4.0 Exploratory Spatial Data Analysis (ESDA)

  • Derive kernel density maps of functional and non-functional water points

  • Display kernel density maps of Osun State

  • Describe spatial patterns displayed by the kernel density maps

4.1 Kernel Density Estimation (KDE)

kde_wpfunc.mppl <- density(wp_functional_ppp, 
                               sigma=bw.ppl, 
                               edge=TRUE)

kde_wpnonfunc.mppl <- density(wp_nonfunctional_ppp,
                             sigma=bw.ppl,
                             edge=TRUE)

par(mfrow=c(1,2))
plot(kde_wpfunc.mppl, 
     main = "Functional Water Points",
     ribside=c("bottom"))
plot(kde_wpnonfunc.mppl,
     main = "Non-Functional Water Points",
     ribside=c("bottom"))

kde_wpfunc.m <- adaptive.density(wp_functional_ppp,
                                  method = "kernel")

kde_wpnonfunc.m <- adaptive.density(wp_nonfunctional_ppp,
                                     method = "kernel")

par(mfrow=c(1,2))
plot(kde_wpfunc.m,
     main = "Functional Water Points",
     ribside=c("right"))
plot(kde_wpnonfunc.m,
     main = "Non-Functional Water Points",
     ribside=c("right"))

4.2.1 Re-Scale KDE values

wp_functional_ppp.km <- rescale(wp_functional_ppp, 1000, "km")

wp_nonfunctional_ppp.km <- rescale(wp_nonfunctional_ppp, 1000, "km")
kde_wpfunc.ppl <- density(wp_functional_ppp.km,
                          sigma = bw.ppl,
                          edge = TRUE)

kde_wpnonfunc.ppl <- density(wp_nonfunctional_ppp.km,
                             sigma = bw.ppl,
                             edge = TRUE)

par(mfrow=c(1,2))
plot(kde_wpfunc.ppl,
     main = "Functional Water Points",
     ribside=c("bottom"))
plot(kde_wpnonfunc.ppl,
     main = "Non-Functional Water Points",
     ribside=c("bottom"))

kde_wpfunc.km <- adaptive.density(wp_functional_ppp.km,
                                  method = "kernel")

kde_wpnonfunc.km <- adaptive.density(wp_nonfunctional_ppp.km,
                                     method = "kernel")

par(mfrow=c(1,2))
plot(kde_wpfunc.km,
     main = "Functional Water Points",
     ribside=c("bottom"))
plot(kde_wpnonfunc.km,
     main = "Non-Functional Water Points",
     ribside=c("bottom"))

4.2.2 Re-Scale KDE values

gridded_wpfunc.ppl <- as.SpatialGridDataFrame.im(kde_wpfunc.ppl)
gridded_wpnonfunc.ppl <- as.SpatialGridDataFrame.im(kde_wpnonfunc.ppl)

spplot(gridded_wpfunc.ppl)

spplot(gridded_wpnonfunc.ppl)

gridded_wpfunc <- as.SpatialGridDataFrame.im(kde_wpfunc.km)
gridded_wpnonfunc <- as.SpatialGridDataFrame.im(kde_wpnonfunc.km)

spplot(gridded_wpfunc)

spplot(gridded_wpnonfunc)

4.2.3 Convert grid object to Raster

kde_wpfunc_raster <- raster(gridded_wpfunc)
kde_wpfunc_raster
class      : RasterLayer 
dimensions : 128, 128, 16384  (nrow, ncol, ncell)
resolution : 0.8948485, 0.9616045  (x, y)
extent     : 176.5032, 291.0438, 331.4347, 454.5201  (xmin, xmax, ymin, ymax)
crs        : NA 
source     : memory
names      : v 
values     : -1.303367e-15, 24.61848  (min, max)
kde_wpnonfunc_raster <- raster(gridded_wpnonfunc)
kde_wpnonfunc_raster
class      : RasterLayer 
dimensions : 128, 128, 16384  (nrow, ncol, ncell)
resolution : 0.8948485, 0.9616045  (x, y)
extent     : 176.5032, 291.0438, 331.4347, 454.5201  (xmin, xmax, ymin, ymax)
crs        : NA 
source     : memory
names      : v 
values     : -1.795722e-16, 21.27264  (min, max)

4.2.4 Assign Projection System

projection(kde_wpfunc_raster) <- CRS("+init=EPSG:26391 +datum:WGS84 +units=km")
kde_wpfunc_raster
class      : RasterLayer 
dimensions : 128, 128, 16384  (nrow, ncol, ncell)
resolution : 0.8948485, 0.9616045  (x, y)
extent     : 176.5032, 291.0438, 331.4347, 454.5201  (xmin, xmax, ymin, ymax)
crs        : +init=EPSG:26391 +datum:WGS84 +units=km 
source     : memory
names      : v 
values     : -1.303367e-15, 24.61848  (min, max)
projection(kde_wpnonfunc_raster) <- CRS("+init=EPSG:26391 +datum:WGS84 +units=km")
kde_wpnonfunc_raster
class      : RasterLayer 
dimensions : 128, 128, 16384  (nrow, ncol, ncell)
resolution : 0.8948485, 0.9616045  (x, y)
extent     : 176.5032, 291.0438, 331.4347, 454.5201  (xmin, xmax, ymin, ymax)
crs        : +init=EPSG:26391 +datum:WGS84 +units=km 
source     : memory
names      : v 
values     : -1.795722e-16, 21.27264  (min, max)

4.2 Visualize KDE

Code 
#tmap generation function
density_map <- function(raster_object, map_title) {
  tmap_mode("view")
  tm_basemap("OpenStreetMap") +
tm_shape(raster_object) +
  tm_raster("v", alpha=0.9) +
  tm_view(set.bounds = c(4,7,5,8),
          set.zoom.limits = c(8, 13))
  } 
density_map(kde_wpfunc_raster)
density_map(kde_wpnonfunc_raster)

4.3 Nearest Neighbor Index

Clarke-Evans Test to check if distribution is random, clustered or dispersed with a 95% confidence interval

4.3.1 Test - Functional Water Points

The test hypotheses for Functional Water Point is :

H0 : The distribution is random

H1 : The distribution is not random

clarkevans.test(wp_functional_ppp,
                correction="none",
                clipregion=NULL,
                alternative=c("clustered"),
                nsim=99)

    Clark-Evans test
    No edge correction
    Monte Carlo test based on 99 simulations of CSR with fixed n

data:  wp_functional_ppp
R = 0.42546, p-value = 0.01
alternative hypothesis: clustered (R < 1)

4.3.2 Test - Non-Functional Water Points

The test hypotheses for Non-Functional Water Point is :

H0 : The distribution is random

H1 : The distribution is not random

clarkevans.test(wp_nonfunctional_ppp,
                correction="none",
                clipregion=NULL,
                alternative=c("clustered"),
                nsim=99)

    Clark-Evans test
    No edge correction
    Monte Carlo test based on 99 simulations of CSR with fixed n

data:  wp_nonfunctional_ppp
R = 0.41415, p-value = 0.01
alternative hypothesis: clustered (R < 1)

4.3.3 Conclusion

Something

5.0 2nd Order Spatial Point Pattern Analysis

5.1 Functional Water Points

5.1.1 F-Function

#G_wp_functional = Gest(wp_functional_ppp, correction = "border")
#plot(G_wp_functional)

Spatial Randomness Test

#plot(G_wp_functional.csr)
#G_wp_functional.csr <- envelope(wp_functional_ppp, Gest, nsim = 999)

5.1.2 K-Function

#K_wp_functional = Kest(wp_functional_ppp, correction = "Ripley")
#plot(K_ck, . -r ~ r, ylab= "K(d)-r", xlab = "d(m)")

Spatial Randomness

#plot(K_wp_functional.csr, . - r ~ r, xlab="d", ylab="K(d)-r", title="K test - Functional")
#K_wp_functional.csr <- envelope(wp_functional_ppp, Kest, nsim = 99, rank = 1, glocal=TRUE)

5.1.3 L-Function

#L_wp_functional = Lest(wp_functional_ppp, correction = "Ripley")
#plot(L_wp_functional_ppp, . -r ~ r, ylab= "L(d)-r", xlab = "d(m)")

Spatial Randomness

#plot(L_wp_functional.csr, . - r ~ r, xlab="d", ylab="L(d)-r")
#L_wp_functional.csr <- envelope(wp_functional_ppp, Lest, nsim = 99, rank = 1, glocal=TRUE)

5.2 Non-Functional Water Points

5.2.1 F-Function

#G_wp_nonfunctional = Gest(wp_functional_ppp, correction = "border")
#plot(G_wp_nonfunctional)

Spatial Randomness Test

#plot(G_wp_nonfunctional.csr)
#G_wp_nonfunctional.csr <- envelope(wp_functional_ppp, Gest, nsim = 999)

5.1.2 K-Function

#K_wp_nonfunctional = Kest(wp_nonfunctional_ppp, correction = "Ripley")
#plot(K_ck, . -r ~ r, ylab= "K(d)-r", xlab = "d(m)")

Spatial Randomness

#plot(K_wp_nonfunctional.csr, . - r ~ r, xlab="d", ylab="K(d)-r", title="K test - Functional")
#K_wp_nonfunctional.csr <- envelope(wp_functional_ppp, Kest, nsim = 99, rank = 1, glocal=TRUE)

5.1.3 L-Function

#L_wp_nonfunctional = Lest(wp_nonfunctional_ppp, correction = "Ripley")
#plot(L_wp_nonfunctional_ppp, . -r ~ r, ylab= "L(d)-r", xlab = "d(m)")

Spatial Randomness

#plot(L_wp_nonfunctional.csr, . - r ~ r, xlab="d", ylab="L(d)-r")
#L_wp_nonfunctional.csr <- envelope(wp_nonfunctional_ppp, Lest, nsim = 99, rank = 1, glocal=TRUE)

5.3 Conclusion

Something

6.0 Spatial Correlation Analysis

6.1 Hypothesis

Something

6.2 Test

nb = include_self(st_knn(st_geometry(wp_sf_nga), 6))

wt = st_kernel_weights(nb, wp_sf_nga, "gaussian", adaptive = TRUE)
functional = wp_sf_nga %>%
  filter(status_clean %in%
           c("Functional",
             "Functional, needs repair",
             "Function, not in use",
             "Functional but not in use"))

A = functional$status_clean
nonfunctional = wp_sf_nga %>%
  filter(status_clean %in%
           c("Abandoned/Decommissioned",
             "Non-Functional, dry",
             "Non-Functional"))
B = nonfunctional$status_clean
LCLQ = local_colocation(A, B, nb, wt, 49)
LCLQ_wp = cbind(wp_sf_nga, LCLQ)
tmap_mode("view")
tm_shape(NGA)+
  tm_polygons()+
tm_shape(LCLQ_wp)+
  tm_dots(col = c("Abandoned.Decommissioned", "Non.Functional", "Non.Functional..dry"),
          size = 0.01,
          border.col = "black",
          border.lwd = 0.5) +
  tm_view(set.zoom.limits = c(8, 13))

6.3 Analysis + Conclusion